Compression of digital speech and audio signals is well known. Compression is generally required to efficiently transmit signals over a communications channel, or to store compressed signals on a digital media device, such as a solid-state memory device or computer hard disk. Although there are many compression (or “coding”) techniques, one method that has remained very popular for digital speech coding is known as Code Excited Linear Prediction (CELP), which is one of a family of “analysis-by-synthesis” coding algorithms. Analysis-by-synthesis generally refers to a coding process by which multiple parameters of a digital model are used to synthesize a set of candidate signals that are compared to an input signal and analyzed for distortion. A set of parameters that yield the lowest distortion is then either transmitted or stored, and eventually used to reconstruct an estimate of the original input signal. CELP is a particular analysis-by-synthesis method that uses one or more codebooks that each essentially comprises sets of code-vectors that are retrieved from the codebook in response to a codebook index.
In modern CELP coders, there is a problem with maintaining high quality speech and audio reproduction at reasonably low data rates. This is especially true for music or other generic audio signals that do not fit the CELP speech model very well. In this case, the model mismatch can cause severely degraded audio quality that can be unacceptable to an end user of the equipment that employs such methods. Therefore, there remains a need for improving performance of CELP type speech coders at low bit rates, especially for music and other non-speech type inputs.